Qualitative comparison
We illustrate the generated samples in Figure REF  for each method. We selected four different fonts including typewriter fonts, artistic fonts as well as handwriting fonts to see the generalization of all competitors. As demonstrated in Figure REF , FSFont are available to recover as many details from reference images. Though other methods like LF-Font [1]} or AGIS-Net [2]} are able to generate stable characters and recover coarse features such as the thickness of strokes and inclination of font, they could not recover details as explicitly as our method does.
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